1. Field of the Invention
The present invention relates to an image processing technique and, more particularly, to a technique of improving the quality of an input image by image correction.
2. Description of the Related Art
In recent years, low-end digital video devices represented by a camera phone have become popular, and users can easily capture and use an enormous number of images. Accordingly, there is a demand for a software method for improving image quality mainly for the purpose of appreciation by increasing the resolution of a low-quality image or correcting a failure image such as a blurred image or the like.
In a known image correction method, for example, conversion dictionaries including feature amounts of low-quality images and those of high-quality images are learned and stored in advance to convert a low-quality image into a corresponding high-quality image (see S. Baker and T. Kanade, “Hallucinating faces”, in IEEE International Conference on Automatic Face and Gesture Recognition, March 2000 (to be referred to as Baker et al. hereinafter)).
An image database that stores an enormous number of images is available recently. Hence, there is proposed a method for complementing an image using a similar image selected from the large number of images. For example, James Hays and Alexei A. Efros, “Scene Completion Using Millions of Photographs”, ACM Transactions on Graphics (SIGGRAPH 2007), August 2007, vol. 26, No. 3 (to be referred to as James et al. hereinafter) describes image correction like patchwork that replaces a region of an input image with a region of another similar image.
The above-described methods are problematic in the following points. The method described in Baker et al. aims at a front face image having normalized size and direction. In this method, a conversion dictionary is stored for each partial region of a face, and image conversion is performed on the position basis. For this reason, it is possible to accurately reconstruct the face image even from a low-quality image in which the shapes of details such as the eye and the mouth are not distinct. However, to apply the method of Baker et al. to a general object in a natural image, it is necessary to correctly cut out the target object from the random background and completely adjust its size and direction for alignment. Such processing is generally hard to execute. Normalizing an articulated object such as a person or an animal that can pose with flexibility is more difficult. It is therefore hard to apply the position-based image correction like the method of Baker et al. to a general object in a free orientation.
In the method of James et al., basically, the original structure of a region is not stored. That is, according to the method of James et al., when a region of an input image is replaced with a region of another similar image, the structure of the input image may be lost, resulting in an image having no correlation between the regions.